There is an increased demand to use Unmanned Aerial Vehicles (UAVs), or drones in general, for many civil applications, e.g., transportation, rescue, and surveillance. At the same time, the risk of illegal use of drones has been greatly raised in terms of privacy violation, spying, and terrorism. The current state of the art involves many drone detection and identification systems that are based on diverse sensing technologies and processing methods. Examples of these features are acoustic sensors; image and video surveillance; and radio frequency (RF) sensors.
With respect to acoustic sensors, drones generate relative loud acoustic noise from their propellers, motors, and engines. Suitable acoustic sensors are used to acquire and analyze drone noise in the time and frequency domains (e.g., frequency spectrum) in order to identify their unique acoustic signature from other noises generated from non-drone sources. These signatures can be evaluated directly and/or compared with pre-stored databases to recognize drone capabilities, manufacturers, and risks. Examples of this approach include U.S. Pat. No. 9,275,645 B2, DE 3929077 C2, and US 2009/0257314 A1. Acoustic sensors are sensitive to background noise that are unavoidable at loud important zones, such as airports and sport events. In addition, certain drone types have weak acoustic noise, in particular at long distances. Problems such as background noise and weak drone-noise extraction can lead to frequent and expensive false detection alarms.
With respect to image and video surveillance, most types of optical images, such as snapshot images, images from video/movies, and thermal images, are utilized to extract signatures for a drone's shape and all appearance aspects. These signatures can be evaluated directly and/or compared with pre-stored databases to recognize drone capabilities, manufacturers, and risks. An example of this approach may be found in U.S. Pat. No. 8,446,321 B2.
With respect to radio frequency (RF) sensors, one of the detection methods is based on detecting wireless RF signals between a drone and its remote control unit. These signals may be control signals and/or a video stream between a drone and a remote unit. By analyzing these signals, relevant signatures can be extracted. These signatures can be evaluated directly and/or compared with pre-stored databases to recognize drone capabilities, manufacturers, and risks. An example of this approach may be found in DE102007062603A1.
Currently available methods are unable to provide detailed information regarding a non-registered drone, like gross weight (including payload), or maintenance health status of a registered drone structure and its rotating parts. Safety regulations for drones in USA and Germany, for example, are based on their weight. The drone weight is an important security feature in order to prevent transporting hazardous materials, such as bombs or explosives, or to prevent spying. Currently, there are no tools or methods to remote monitor in-flight payload of drones to check pre-registered limits. The design payload of standard commercial drones can be greatly increased by simple design modifications, e.g., by changing the propeller's size and/or driving motors. Such modifications may significantly increase payload capacity to carry dangerous materials without triggering aforementioned drone detection systems. In addition, the maintenance health status information, such as components' wear and degradation, faults, or component failures, are also important to predict and avoid vehicle failure and related accidents.